Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings

By Max Conti, Manuel Faysse, Gautier Viaud, Antoine Bosselut, C. Hudelot, Pierre Colombo. 2025

Subfields: Information Retrieval & Search, Representation & Self-Supervised Learning

Citations in the corpus

• NEAR: Neighborhood-Aware Residuals for Memory Retrieval (2026, unknown): “Contextual embedding methods incorporate surrounding text or other documents during encoding (Morris & Rush, 2025; Conti et al., 2025).”
• ModernVBERT: Towards Smaller Visual Document Retrievers (2025, unknown): “The post-training dataset used as starting point in our ablations comprises 118k document-query pairs from the ColPali corpus Faysse et al. (2025) as well as another 118k of natural image-description pairs from the MSCOCO train set (Lin et al., 2015).”